학술논문

Improved SEIR prediction model based on particle swarm optimization
Document Type
Conference
Source
2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) Image Processing, Electronics and Computers (IPEC), 2021 IEEE Asia-Pacific Conference on. :733-737 Apr, 2021
Subject
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
COVID-19
Epidemics
Urban areas
Transportation
Differential equations
Predictive models
Prediction algorithms
SEIR model
Particle swarm optimization algorithm
Language
Abstract
This article analyzes the epidemic situation in all provinces of China, considering the impact of population mobility in all provinces during the Spring Festival transportation without restricting people's free movement in Wuhan. According to the characteristics of novel Coronavirus, we choose the SEIR model to establish the required differential equations. On this basis, we establish different differential equations according to the characteristics of Wuhan and all provinces of China. By collecting relevant data and using particle swarm optimization algorithm for data fitting, we predict the epidemic situation of domestic provinces during the period from the first case to March $1^{\mathrm{s}\mathrm{t}}$ while Wuhan is not sealed off, which reflects the necessity of Wuhan sealed off.